Motoaki MOURI

Our research group has been measuring Extremely Low Frequency (ELF) magnetic fields across Japan. The ELF measurements are mixtures of signals associated with various natural or artificial phenomena. When focus on specific factor, the signals related to other factors distort analysis result. In order to get specific information accurately, we should estimate desired signals or eliminate undesired signals. We found that Image Space Reconstruction Algorithm (ISRA), one of the Nonnegative Matrix Factorization (NMF) algorithm, works better than independent component analysis to estimate the ELF background signal. However, ISRA sometimes failed to estimate the weight vector for the background signal. We considered that ISRA has weakness for outliers and sparse signals because ISRA is based on minimizing L2 (Frobenius) norm between input matrix and projected matrix from estimated matrices. In order to improve robustness, we developed new methods based on minimizing quasi-L1 norm (QL1-NMF). In the experiment using generated signals and ELF observed signals which had outliers, the proposed method estimate background signal more accurately than ISRA and other L1 norm based algorithms.

Saccade-related electroencephalogram (EEG) signals have been the subject of application oriented research by our group toward developing a brain computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals on-line. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. In signal processing method for BCI, raw EEG signals are analyzed. In ensemble averaging method which is major EEG analysis is not suitable for processing raw EEG signals. In order to process raw EEG data, we use independent component analysis. This paper presents extraction rate of saccade-related EEG signals by four ICA algorithms and six window size. As results of extracting rate focused on ICA algorithm, The JADE and Fast ICA have good results. As you know, calucuration time in Fast ICA is fater than calucuration time in JADE. Therefore, in this case, Fast ICA is best in order to extract saccade-related ICs. Next, we focus on extracting rate in each windows. The windows not including EEG signals after saccade has and the windows which has small window size is good extracting rate.

Anomalous environmental electromagnetic (EM) radiation waves have been reported as the portents of earthquakes. Our study's goal is predicting earthquakes using EM radiation waves by detecting some anomalies. We have been measuring the Extremely Low Frequency (ELF) range EM radiation waves all over Japan. However, the recorded data contain signals unrelated to earthquakes. These signals, as noise, confound earthquake prediction efforts. In this paper, we propose an efficient method of global signal elimination and enhancement local signals using Independent Component Analysis (ICA). We evaluated the effectiveness of this method.